bpcluster.asv

人工神经网络MATLAB源程序:此MATLAB程序用于人工神经网络

%输入色谱数据P和对应的目标期望值T
b=[ 0.5709    0.0439    0.4996    0.2377    2.3461    0.0423
    1.0496    0.3980    1.6184    0.1573    0.9451    1.5614
    0.6880    0.2292    1.1794    1.1895    0.5214    0.2384
    0.6540    0.2017    1.6566    0.5587    0.4112    1.2298
    2.0277    0.3553    1.8477    1.1107    0.1547    0.2531
    1.5818    0.4356    1.6425    1.2554    0.2268    0.5861
    0.5384    0.4898    0.4886    0.4446    0.1228    0.8452
    0.1734    0.1885    0.0966    0.1141    0.0819    0.2619
    0.1394    0.3331    0.0812    0.0585    0.0935    0.3528
    0.3665    0.1494    0.3549    0.2927    0.1035    0.1513
    0.1424    0.2386    0.1759    0.1476    0.4943    0.6602
    0.5203    0.3955    0.5274    0.2994    0.2422    0.4497
    0.3003    1.2505    0.6264    0.6104    0.0731    0.9278
    1.0000    1.0000    1.0000    1.0000    1.0000    1.0000
    1.1268    0.1269    1.3431    0.6588    0.1034    0.3995];
 

P=b(:,[1,2,3,5]);
T=[1 0 1 2];
x=[1 1 1 1];
%绘出训练样品数据
figure;
plot(T,x,'r*')
%设置网络隐单元的神经元数(可
 n=6;
%建立BP网络
net=newff(minmax(P),[n,1],{'tansig','logsig'},'trainlm');
%训练建立的网络
 net.trainParam.show=50;  
 net.trainParam.lr=0.05;
 net.trainParam.mc=0.9;
 net.trainParam.epochs=2000;
 net.trainParam.goal=0.001;
%调用TRAINGDM算法训练BP网络
 net=train(net,P,T);
%对BP网络进行仿真,并计算误差
 A=sim(net,P)
 E=A-T;
 M=sse(E)
 N=mse(E)
%对建立的网络进行仿真,对未知的样品进行分类
p1=b(:,[4,6]);
y=sim(net,p1)
%绘出仿真样品数据
z=[1 1];
figure;
plot(T,x,'r*')
hold on
plot(y,z,'bp')